SOTAVerified

Relational Reasoning

The goal of Relational Reasoning is to figure out the relationships among different entities, such as image pixels, words or sentences, human skeletons or interactive moving agents.

Source: Social-WaGDAT: Interaction-aware Trajectory Prediction via Wasserstein Graph Double-Attention Network

Papers

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Showing 201–210 of 483 papers

TitleStatusHype
Imagine, Reason and Write: Visual Storytelling with Graph Knowledge and Relational Reasoning—0
Relational Learning with Gated and Attentive Neighbor Aggregator for Few-Shot Knowledge Graph CompletionCode1
Agent-Centric Representations for Multi-Agent Reinforcement Learning—0
Object-Centric Representation Learning for Video Question Answering—0
Unified Graph Structured Models for Video Understanding—0
Graph-based Facial Affect Analysis: A Review—0
Prototypical Representation Learning for Relation ExtractionCode1
Automatic Generation of Contrast Sets from Scene Graphs: Probing the Compositional Consistency of GQACode0
Inductive Relation Prediction by BERTCode1
A Relational-learning Perspective to Multi-label Chest X-ray Classification—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified